History of Dictionary Searches using Damerau-Levenshtein distance in T-SQL
Fuzzy-string Searches
(up to 100 most recent)
for
"forecastles"
| Num | Started At (CA time) | Searched Word | Change Limit | Words Checked | Words Matched | Seconds | Words Per Sec |
| 377 | 2026-04-09 07:35:00 | forecastles | 1 | 49908 | 2 | 3.920 | 12731.6 |
| 376 | 2026-04-09 07:34:47 | forecastles | 1 | 49908 | 2 | 1.953 | 25554.5 |
| 375 | 2026-03-31 13:41:35 | forecastles | 5 | 164607 | 478 | 73.553 | 2237.9 |
| 374 | 2026-03-31 09:23:48 | forecastles | 5 | 164607 | 478 | 117.680 | 1398.8 |
| 373 | 2026-03-31 09:24:15 | forecastles | 5 | 164607 | 478 | 66.316 | 2482.2 |
| 372 | 2026-03-31 09:22:53 | forecastles | 5 | 164607 | 478 | 120.566 | 1365.3 |
| 371 | 2026-03-31 09:22:09 | forecastles | 5 | 164607 | 478 | 77.813 | 2115.4 |
| 370 | 2026-03-31 09:21:12 | forecastles | 5 | 164607 | 478 | 98.753 | 1666.9 |
| 369 | 2026-03-31 09:19:59 | forecastles | 5 | 164607 | 478 | 78.813 | 2088.6 |
| 368 | 2026-03-31 09:19:03 | forecastles | 5 | 164607 | 478 | 78.930 | 2085.5 |
| 367 | 2026-03-31 09:18:55 | forecastles | 5 | 164607 | 478 | 77.696 | 2118.6 |
| 366 | 2026-03-31 09:17:55 | forecastles | 5 | 164607 | 478 | 80.230 | 2051.7 |
| 365 | 2026-03-31 09:17:12 | forecastles | 5 | 164607 | 478 | 87.506 | 1881.1 |
| 364 | 2026-03-31 09:16:34 | forecastles | 5 | 164607 | 478 | 115.036 | 1430.9 |
| 363 | 2026-03-29 22:28:31 | forecastles | 1 | 49908 | 2 | 6.046 | 8254.7 |
| 362 | 2026-03-25 16:08:09 | forecastles | 5 | 164607 | 478 | 76.426 | 2153.8 |
| 361 | 2026-03-21 17:13:22 | forecastles | 1 | 49908 | 2 | 3.766 | 13252.3 |
| 360 | 2026-03-21 12:15:58 | forecastles | 5 | 164607 | 478 | 73.190 | 2249.0 |
| 359 | 2026-03-18 14:12:48 | forecastles | 1 | 49908 | 2 | 1.626 | 30693.7 |
| 358 | 2026-03-16 02:22:35 | forecastles | 5 | 164607 | 478 | 16.860 | 9763.2 |
| 357 | 2026-03-10 01:11:11 | forecastles | 1 | 49908 | 2 | 0.940 | 53093.6 |
| 356 | 2026-03-04 09:29:31 | forecastles | 1 | 49908 | 2 | 0.873 | 57168.4 |
| 355 | 2026-02-25 14:40:13 | forecastles | 1 | 49908 | 2 | 0.846 | 58992.9 |
| 354 | 2026-02-23 10:56:47 | forecastles | 5 | 164607 | 478 | 23.956 | 6871.2 |
| 353 | 2026-02-18 19:51:01 | forecastles | 5 | 164607 | 478 | 29.330 | 5612.2 |
| 352 | 2026-02-14 15:08:21 | forecastles | 3 | 121633 | 15 | 17.953 | 6775.1 |
| 351 | 2026-02-14 15:07:55 | forecastles | 3 | 121633 | 15 | 22.690 | 5360.6 |
| 350 | 2026-02-14 15:07:38 | forecastles | 3 | 121633 | 15 | 13.783 | 8824.9 |
| 349 | 2026-02-10 12:36:56 | forecastles | 3 | 121633 | 15 | 21.063 | 5774.7 |
| 348 | 2026-02-07 00:24:44 | forecastles | 5 | 164607 | 478 | 19.500 | 8441.4 |
| 347 | 2026-02-07 00:24:28 | forecastles | 5 | 164607 | 478 | 21.173 | 7774.4 |
| 346 | 2026-02-07 00:24:13 | forecastles | 5 | 164607 | 478 | 17.346 | 9489.6 |
| 345 | 2026-02-06 18:16:19 | forecastles | 5 | 164607 | 478 | 16.360 | 10061.6 |
| 344 | 2026-02-04 01:55:12 | forecastles | 5 | 164607 | 478 | 16.453 | 10004.7 |
| 343 | 2026-02-03 11:53:54 | forecastles | 3 | 121633 | 15 | 9.843 | 12357.3 |
| 342 | 2026-02-03 11:10:58 | forecastles | 2 | 86808 | 7 | 2.110 | 41141.2 |
| 341 | 2026-02-01 16:38:23 | forecastles | 5 | 164607 | 478 | 14.376 | 11450.1 |
| 340 | 2026-02-01 14:40:21 | forecastles | 2 | 86808 | 7 | 2.310 | 37579.2 |
| 339 | 2026-02-01 12:57:29 | forecastles | 4 | 148819 | 68 | 19.873 | 7488.5 |
| 338 | 2026-02-01 10:53:58 | forecastles | 1 | 49908 | 2 | 0.876 | 56972.6 |
| 337 | 2026-01-22 15:11:33 | forecastles | 1 | 49908 | 2 | 0.856 | 58303.7 |
| 336 | 2026-01-17 06:15:13 | forecastles | 1 | 49908 | 2 | 0.780 | 63984.6 |
| 335 | 2025-11-28 14:08:00 | forecastles | 1 | 49908 | 2 | 1.673 | 29831.4 |
| 334 | 2025-11-27 15:38:19 | forecastles | 1 | 49908 | 2 | 0.840 | 59414.3 |
| 333 | 2025-11-25 11:02:45 | forecastles | 2 | 86808 | 7 | 9.626 | 9018.1 |
| 332 | 2025-11-16 02:51:18 | forecastles | 3 | 121633 | 15 | 5.266 | 23097.8 |
| 331 | 2025-11-06 10:49:02 | forecastles | 1 | 49908 | 2 | 0.826 | 60421.3 |
| 330 | 2025-10-12 07:03:08 | forecastles | 1 | 49908 | 2 | 0.860 | 58032.6 |
| 329 | 2025-09-18 10:08:06 | forecastles | 1 | 49908 | 2 | 0.843 | 59202.8 |
| 328 | 2025-08-26 19:10:52 | forecastles | 2 | 86808 | 7 | 2.763 | 31418.0 |
| 327 | 2025-08-25 21:52:34 | forecastles | 1 | 49908 | 2 | 0.876 | 56972.6 |
| 326 | 2025-08-17 04:19:39 | forecastles | 1 | 49908 | 2 | 1.936 | 25778.9 |
| 325 | 2025-07-25 05:45:19 | forecastles | 1 | 49908 | 2 | 5.580 | 8944.1 |
| 324 | 2025-07-24 05:27:48 | forecastles | 1 | 49908 | 2 | 3.860 | 12929.5 |
| 323 | 2025-07-18 17:19:31 | forecastles | 1 | 49908 | 2 | 5.076 | 9832.2 |
| 322 | 2025-07-10 22:17:38 | forecastles | 3 | 121633 | 15 | 33.050 | 3680.3 |
| 321 | 2025-07-10 16:48:42 | forecastles | 3 | 121633 | 15 | 31.610 | 3847.9 |
| 320 | 2025-07-10 13:59:56 | forecastles | 2 | 86808 | 7 | 15.376 | 5645.7 |
| 319 | 2025-07-10 12:51:33 | forecastles | 2 | 86808 | 7 | 11.953 | 7262.4 |
| 318 | 2025-07-09 15:54:47 | forecastles | 1 | 49908 | 2 | 0.860 | 58032.6 |
| 317 | 2025-06-01 02:11:26 | forecastles | 1 | 49908 | 2 | 3.280 | 15215.9 |
| 316 | 2025-05-29 07:14:11 | forecastles | 1 | 49908 | 2 | 2.640 | 18904.5 |
| 315 | 2025-05-26 21:13:04 | forecastles | 1 | 49908 | 2 | 2.300 | 21699.1 |
| 314 | 2025-05-26 07:47:54 | forecastles | 2 | 86808 | 7 | 6.156 | 14101.4 |
| 313 | 2025-05-24 11:21:56 | forecastles | 3 | 121633 | 15 | 30.486 | 3989.8 |
| 312 | 2025-05-23 20:56:13 | forecastles | 3 | 121633 | 15 | 29.143 | 4173.7 |
| 311 | 2025-05-23 19:10:12 | forecastles | 2 | 86808 | 7 | 18.156 | 4781.2 |
| 310 | 2025-05-22 16:09:52 | forecastles | 3 | 121633 | 15 | 17.080 | 7121.4 |
| 309 | 2025-05-22 14:10:36 | forecastles | 3 | 121633 | 15 | 19.236 | 6323.2 |
| 308 | 2025-05-22 08:01:17 | forecastles | 2 | 86808 | 7 | 6.453 | 13452.3 |
| 307 | 2025-05-21 15:54:19 | forecastles | 1 | 49908 | 2 | 2.110 | 23653.1 |
| 306 | 2025-05-21 05:13:51 | forecastles | 1 | 49908 | 2 | 0.890 | 56076.4 |
| 305 | 2025-04-24 06:17:37 | forecastles | 1 | 49908 | 2 | 4.560 | 10944.7 |
| 304 | 2025-04-23 10:19:35 | forecastles | 3 | 121633 | 15 | 31.046 | 3917.8 |
| 303 | 2025-04-18 10:30:52 | forecastles | 3 | 121633 | 15 | 17.113 | 7107.6 |
| 302 | 2025-03-24 08:36:19 | forecastles | 1 | 49908 | 2 | 2.046 | 24393.0 |
| 301 | 2025-03-22 23:23:27 | forecastles | 1 | 49908 | 2 | 4.080 | 12232.4 |
| 300 | 2025-03-22 21:58:23 | forecastles | 3 | 121633 | 15 | 32.656 | 3724.7 |
| 299 | 2025-03-22 20:47:08 | forecastles | 2 | 86808 | 7 | 12.393 | 7004.6 |
| 298 | 2025-03-22 11:36:18 | forecastles | 3 | 121633 | 15 | 21.520 | 5652.1 |
| 297 | 2025-03-22 09:16:45 | forecastles | 1 | 49908 | 2 | 2.983 | 16730.8 |
| 296 | 2025-03-22 07:09:23 | forecastles | 2 | 86808 | 7 | 15.050 | 5768.0 |
| 295 | 2025-03-22 00:32:47 | forecastles | 4 | 148819 | 68 | 29.986 | 4962.9 |
| 294 | 2025-03-22 00:17:33 | forecastles | 4 | 148819 | 68 | 67.393 | 2208.2 |
| 293 | 2025-03-20 20:03:10 | forecastles | 4 | 148819 | 68 | 9.326 | 15957.4 |
| 292 | 2025-03-18 01:39:30 | forecastles | 4 | 148819 | 68 | 32.300 | 4607.4 |
| 291 | 2025-03-13 07:27:01 | forecastles | 1 | 49908 | 2 | 2.046 | 24393.0 |
| 290 | 2025-03-13 01:46:45 | forecastles | 4 | 148819 | 68 | 49.300 | 3018.6 |
| 289 | 2025-03-11 07:48:16 | forecastles | 4 | 148819 | 68 | 49.723 | 2993.0 |
| 288 | 2025-03-01 14:11:46 | forecastles | 1 | 49908 | 2 | 3.170 | 15743.8 |
| 287 | 2025-02-19 17:48:46 | forecastles | 1 | 49908 | 2 | 3.936 | 12679.9 |
| 286 | 2025-02-18 11:43:29 | forecastles | 4 | 148819 | 68 | 56.490 | 2634.4 |
| 285 | 2025-02-07 18:24:47 | forecastles | 3 | 121633 | 15 | 24.440 | 4976.8 |
| 284 | 2025-02-07 18:24:41 | forecastles | 2 | 86808 | 7 | 7.500 | 11574.4 |
| 283 | 2025-02-07 18:23:14 | forecastles | 1 | 49908 | 2 | 3.406 | 14653.0 |
| 282 | 2025-02-01 04:31:30 | forecastles | 4 | 148819 | 68 | 47.096 | 3159.9 |
| 281 | 2025-01-20 18:07:24 | forecastles | 4 | 148819 | 68 | 53.000 | 2807.9 |
| 280 | 2025-01-19 20:26:43 | forecastles | 4 | 148819 | 68 | 54.630 | 2724.1 |
| 279 | 2025-01-18 22:57:23 | forecastles | 4 | 148819 | 68 | 51.943 | 2865.0 |
| 278 | 2025-01-18 22:57:24 | forecastles | 4 | 148819 | 68 | 46.736 | 3184.2 |